Abstract—Semi-nonnegative matrix factorization (Semi-NMF) is one of variations of nonnegative matrix factorization model (NMF) when the data matrix X is unconstrained (it may have mixed signs). Semi-NMF decomposes X into two matrices A and B of dimensions n×k and k × p respectively, where each element of the matrix B is nonnegative, such that: X ≈ AB . In the present paper, we proposed a semi-nonnegative matrix factorization algorithm based on genetic algorithm (GA) initialization which has larger searching area and gives the best initialization for the Semi-NMF algorithm to get the optimal solution of semi-nonnegative matrix factorization problem. Also, we compared this initialization for Semi-NMF algorithm with both the random and the k-means initializations introduced in the literature.
Index Terms—Semi-nonnegative matrix factorization, genetic algorithm, initialization.
The authors are with Faculté de mathématiques, USTHB, El-Alia BP 32, Bab-Ezzouar 16111, Alger, Algérie (firstname.lastname@example.org, email@example.com).
Cite: M. Chouh and K. Boukhetala, "Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization," International Journal of Machine Learning and Computing vol. 6, no. 4, pp. 231-234, 2016.